Overview

Dataset statistics

Number of variables14
Number of observations60
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.2 KiB
Average record size in memory88.0 B

Variable types

Categorical1
Numeric13

Warnings

Team has a high cardinality: 60 distinct values High cardinality
Tournament is highly correlated with Score and 11 other fieldsHigh correlation
Score is highly correlated with Tournament and 11 other fieldsHigh correlation
PlayedGames is highly correlated with Tournament and 11 other fieldsHigh correlation
WonGames is highly correlated with Tournament and 11 other fieldsHigh correlation
DrawnGames is highly correlated with Tournament and 11 other fieldsHigh correlation
LostGames is highly correlated with Tournament and 11 other fieldsHigh correlation
BasketScored is highly correlated with Tournament and 11 other fieldsHigh correlation
BasketGiven is highly correlated with Tournament and 11 other fieldsHigh correlation
TeamLaunch is highly correlated with Tournament and 8 other fieldsHigh correlation
HighestPositionHeld is highly correlated with Tournament and 11 other fieldsHigh correlation
WinRatio is highly correlated with Tournament and 10 other fieldsHigh correlation
LossRatio is highly correlated with Tournament and 10 other fieldsHigh correlation
WinLossDiffrence is highly correlated with Tournament and 10 other fieldsHigh correlation
Tournament is highly correlated with Score and 11 other fieldsHigh correlation
Score is highly correlated with Tournament and 11 other fieldsHigh correlation
PlayedGames is highly correlated with Tournament and 11 other fieldsHigh correlation
WonGames is highly correlated with Tournament and 11 other fieldsHigh correlation
DrawnGames is highly correlated with Tournament and 11 other fieldsHigh correlation
LostGames is highly correlated with Tournament and 11 other fieldsHigh correlation
BasketScored is highly correlated with Tournament and 11 other fieldsHigh correlation
BasketGiven is highly correlated with Tournament and 11 other fieldsHigh correlation
TeamLaunch is highly correlated with Tournament and 9 other fieldsHigh correlation
HighestPositionHeld is highly correlated with Tournament and 11 other fieldsHigh correlation
WinRatio is highly correlated with Tournament and 11 other fieldsHigh correlation
LossRatio is highly correlated with Tournament and 10 other fieldsHigh correlation
WinLossDiffrence is highly correlated with Tournament and 10 other fieldsHigh correlation
Tournament is highly correlated with Score and 10 other fieldsHigh correlation
Score is highly correlated with Tournament and 10 other fieldsHigh correlation
PlayedGames is highly correlated with Tournament and 10 other fieldsHigh correlation
WonGames is highly correlated with Tournament and 10 other fieldsHigh correlation
DrawnGames is highly correlated with Tournament and 10 other fieldsHigh correlation
LostGames is highly correlated with Tournament and 10 other fieldsHigh correlation
BasketScored is highly correlated with Tournament and 10 other fieldsHigh correlation
BasketGiven is highly correlated with Tournament and 10 other fieldsHigh correlation
TeamLaunch is highly correlated with HighestPositionHeldHigh correlation
HighestPositionHeld is highly correlated with Tournament and 11 other fieldsHigh correlation
WinRatio is highly correlated with Tournament and 10 other fieldsHigh correlation
LossRatio is highly correlated with Tournament and 10 other fieldsHigh correlation
WinLossDiffrence is highly correlated with Tournament and 10 other fieldsHigh correlation
Tournament is highly correlated with WinRatio and 10 other fieldsHigh correlation
WinRatio is highly correlated with Tournament and 11 other fieldsHigh correlation
BasketGiven is highly correlated with Tournament and 10 other fieldsHigh correlation
DrawnGames is highly correlated with Tournament and 10 other fieldsHigh correlation
WinLossDiffrence is highly correlated with Tournament and 11 other fieldsHigh correlation
WonGames is highly correlated with Tournament and 10 other fieldsHigh correlation
Team is highly correlated with Tournament and 12 other fieldsHigh correlation
BasketScored is highly correlated with Tournament and 10 other fieldsHigh correlation
Score is highly correlated with Tournament and 10 other fieldsHigh correlation
TeamLaunch is highly correlated with Team and 1 other fieldsHigh correlation
PlayedGames is highly correlated with Tournament and 10 other fieldsHigh correlation
LossRatio is highly correlated with Tournament and 11 other fieldsHigh correlation
HighestPositionHeld is highly correlated with WinRatio and 5 other fieldsHigh correlation
LostGames is highly correlated with Tournament and 11 other fieldsHigh correlation
Team is uniformly distributed Uniform
Team has unique values Unique
Score has unique values Unique
BasketGiven has unique values Unique
WinLossDiffrence has unique values Unique

Reproduction

Analysis started2021-09-08 06:11:02.774435
Analysis finished2021-09-08 06:11:24.686177
Duration21.91 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

Team
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct60
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size960.0 B
Team 5
 
1
Team 12
 
1
Team 22
 
1
Team 6
 
1
Team 30
 
1
Other values (55)
55 

Length

Max length7
Median length7
Mean length6.85
Min length6

Characters and Unicode

Total characters411
Distinct characters15
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique60 ?
Unique (%)100.0%

Sample

1st rowTeam 1
2nd rowTeam 2
3rd rowTeam 3
4th rowTeam 4
5th rowTeam 5

Common Values

ValueCountFrequency (%)
Team 51
 
1.7%
Team 121
 
1.7%
Team 221
 
1.7%
Team 61
 
1.7%
Team 301
 
1.7%
Team 81
 
1.7%
Team 21
 
1.7%
Team 361
 
1.7%
Team 141
 
1.7%
Team 491
 
1.7%
Other values (50)50
83.3%

Length

2021-09-08T11:41:24.918949image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
team60
50.0%
241
 
0.8%
411
 
0.8%
561
 
0.8%
201
 
0.8%
71
 
0.8%
391
 
0.8%
291
 
0.8%
211
 
0.8%
41
 
0.8%
Other values (51)51
42.5%

Most occurring characters

ValueCountFrequency (%)
T60
14.6%
e60
14.6%
a60
14.6%
m60
14.6%
60
14.6%
116
 
3.9%
216
 
3.9%
316
 
3.9%
416
 
3.9%
516
 
3.9%
Other values (5)31
7.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter180
43.8%
Decimal Number111
27.0%
Uppercase Letter60
 
14.6%
Space Separator60
 
14.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
116
14.4%
216
14.4%
316
14.4%
416
14.4%
516
14.4%
67
6.3%
76
 
5.4%
86
 
5.4%
96
 
5.4%
06
 
5.4%
Lowercase Letter
ValueCountFrequency (%)
e60
33.3%
a60
33.3%
m60
33.3%
Uppercase Letter
ValueCountFrequency (%)
T60
100.0%
Space Separator
ValueCountFrequency (%)
60
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin240
58.4%
Common171
41.6%

Most frequent character per script

Common
ValueCountFrequency (%)
60
35.1%
116
 
9.4%
216
 
9.4%
316
 
9.4%
416
 
9.4%
516
 
9.4%
67
 
4.1%
76
 
3.5%
86
 
3.5%
96
 
3.5%
Latin
ValueCountFrequency (%)
T60
25.0%
e60
25.0%
a60
25.0%
m60
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII411
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T60
14.6%
e60
14.6%
a60
14.6%
m60
14.6%
60
14.6%
116
 
3.9%
216
 
3.9%
316
 
3.9%
416
 
3.9%
516
 
3.9%
Other values (5)31
7.5%

Tournament
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct33
Distinct (%)55.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.38333333
Minimum1
Maximum86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size960.0 B
2021-09-08T11:41:25.031648image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median12
Q339
95-th percentile82.2
Maximum86
Range85
Interquartile range (IQR)35

Descriptive statistics

Standard deviation26.88461955
Coefficient of variation (CV)1.1025818
Kurtosis0.1761347883
Mean24.38333333
Median Absolute Deviation (MAD)9.5
Skewness1.197176233
Sum1463
Variance722.7827684
MonotonicityNot monotonic
2021-09-08T11:41:25.139361image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
45
 
8.3%
15
 
8.3%
124
 
6.7%
24
 
6.7%
34
 
6.7%
863
 
5.0%
63
 
5.0%
172
 
3.3%
92
 
3.3%
512
 
3.3%
Other values (23)26
43.3%
ValueCountFrequency (%)
15
8.3%
24
6.7%
34
6.7%
45
8.3%
51
 
1.7%
63
5.0%
72
 
3.3%
92
 
3.3%
112
 
3.3%
124
6.7%
ValueCountFrequency (%)
863
5.0%
822
3.3%
801
 
1.7%
731
 
1.7%
701
 
1.7%
581
 
1.7%
512
3.3%
451
 
1.7%
441
 
1.7%
431
 
1.7%

Score
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct60
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean916.45
Minimum14
Maximum4385
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size720.0 B
2021-09-08T11:41:25.268051image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile33.4
Q1104.25
median395.5
Q31360.5
95-th percentile3388.8
Maximum4385
Range4371
Interquartile range (IQR)1256.25

Descriptive statistics

Standard deviation1138.342899
Coefficient of variation (CV)1.24212221
Kurtosis1.686503264
Mean916.45
Median Absolute Deviation (MAD)332
Skewness1.574103973
Sum54987
Variance1295824.557
MonotonicityStrictly decreasing
2021-09-08T11:41:25.395743image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28191
 
1.7%
1321
 
1.7%
341
 
1.7%
34421
 
1.7%
13511
 
1.7%
221
 
1.7%
9701
 
1.7%
761
 
1.7%
4211
 
1.7%
401
 
1.7%
Other values (50)50
83.3%
ValueCountFrequency (%)
141
1.7%
191
1.7%
221
1.7%
341
1.7%
351
1.7%
401
1.7%
421
1.7%
521
1.7%
561
1.7%
711
1.7%
ValueCountFrequency (%)
43851
1.7%
42621
1.7%
34421
1.7%
33861
1.7%
33681
1.7%
28191
1.7%
27921
1.7%
25731
1.7%
21091
1.7%
18841
1.7%

PlayedGames
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct52
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean810.1
Minimum30
Maximum2762
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size720.0 B
2021-09-08T11:41:25.526152image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile37.6
Q1115.5
median424.5
Q31345.5
95-th percentile2668.9
Maximum2762
Range2732
Interquartile range (IQR)1230

Descriptive statistics

Standard deviation877.4653926
Coefficient of variation (CV)1.083156885
Kurtosis-0.03039849115
Mean810.1
Median Absolute Deviation (MAD)344.5
Skewness1.123453799
Sum48606
Variance769945.5153
MonotonicityNot monotonic
2021-09-08T11:41:25.644439image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27623
 
5.0%
303
 
5.0%
682
 
3.3%
382
 
3.3%
802
 
3.3%
1142
 
3.3%
7421
 
1.7%
10961
 
1.7%
2041
 
1.7%
3341
 
1.7%
Other values (42)42
70.0%
ValueCountFrequency (%)
303
5.0%
382
3.3%
541
 
1.7%
682
3.3%
721
 
1.7%
802
3.3%
901
 
1.7%
1081
 
1.7%
1142
3.3%
1161
 
1.7%
ValueCountFrequency (%)
27623
5.0%
26641
 
1.7%
26261
 
1.7%
26141
 
1.7%
24081
 
1.7%
23021
 
1.7%
19861
 
1.7%
17281
 
1.7%
16981
 
1.7%
15301
 
1.7%

WonGames
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct58
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean309.0333333
Minimum5
Maximum1647
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size720.0 B
2021-09-08T11:41:25.774136image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile7.95
Q134.75
median124
Q3432.75
95-th percentile1210.6
Maximum1647
Range1642
Interquartile range (IQR)398

Descriptive statistics

Standard deviation408.4813946
Coefficient of variation (CV)1.321803671
Kurtosis2.577188927
Mean309.0333333
Median Absolute Deviation (MAD)104.5
Skewness1.786066522
Sum18542
Variance166857.0497
MonotonicityNot monotonic
2021-09-08T11:41:25.907074image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72
 
3.3%
82
 
3.3%
1291
 
1.7%
12411
 
1.7%
961
 
1.7%
621
 
1.7%
4531
 
1.7%
9481
 
1.7%
5861
 
1.7%
2031
 
1.7%
Other values (48)48
80.0%
ValueCountFrequency (%)
51
1.7%
72
3.3%
82
3.3%
131
1.7%
171
1.7%
181
1.7%
191
1.7%
201
1.7%
211
1.7%
261
1.7%
ValueCountFrequency (%)
16471
1.7%
15811
1.7%
12411
1.7%
12091
1.7%
11871
1.7%
9901
1.7%
9481
1.7%
8641
1.7%
6981
1.7%
6061
1.7%

DrawnGames
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct56
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean192.0833333
Minimum4
Maximum633
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size720.0 B
2021-09-08T11:41:26.050187image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile7.9
Q126.25
median98.5
Q3331.5
95-th percentile598.5
Maximum633
Range629
Interquartile range (IQR)305.25

Descriptive statistics

Standard deviation201.9855081
Coefficient of variation (CV)1.051551452
Kurtosis-0.4223342667
Mean192.0833333
Median Absolute Deviation (MAD)82.5
Skewness0.98489917
Sum11525
Variance40798.14548
MonotonicityNot monotonic
2021-09-08T11:41:26.174289image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
952
 
3.3%
442
 
3.3%
142
 
3.3%
162
 
3.3%
921
 
1.7%
5731
 
1.7%
631
 
1.7%
5771
 
1.7%
3271
 
1.7%
3301
 
1.7%
Other values (46)46
76.7%
ValueCountFrequency (%)
41
1.7%
51
1.7%
61
1.7%
81
1.7%
101
1.7%
111
1.7%
131
1.7%
142
3.3%
162
3.3%
181
1.7%
ValueCountFrequency (%)
6331
1.7%
6161
1.7%
6081
1.7%
5981
1.7%
5771
1.7%
5731
1.7%
5521
1.7%
5311
1.7%
5221
1.7%
4401
1.7%

LostGames
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct55
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean308.8166667
Minimum15
Maximum1070
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size720.0 B
2021-09-08T11:41:26.315263image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile19.95
Q162.75
median197.5
Q3563.5
95-th percentile862.3
Maximum1070
Range1055
Interquartile range (IQR)500.75

Descriptive statistics

Standard deviation294.5086394
Coefficient of variation (CV)0.9536682156
Kurtosis-0.4560384548
Mean308.8166667
Median Absolute Deviation (MAD)158.5
Skewness0.8805955594
Sum18529
Variance86735.3387
MonotonicityNot monotonic
2021-09-08T11:41:26.452188image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
373
 
5.0%
8612
 
3.3%
2952
 
3.3%
662
 
3.3%
1181
 
1.7%
901
 
1.7%
631
 
1.7%
1941
 
1.7%
1971
 
1.7%
1981
 
1.7%
Other values (45)45
75.0%
ValueCountFrequency (%)
151
 
1.7%
181
 
1.7%
191
 
1.7%
201
 
1.7%
211
 
1.7%
301
 
1.7%
331
 
1.7%
373
5.0%
411
 
1.7%
441
 
1.7%
ValueCountFrequency (%)
10701
1.7%
9201
1.7%
8871
1.7%
8612
3.3%
7751
1.7%
7661
1.7%
7231
1.7%
6821
1.7%
6391
1.7%
6291
1.7%

BasketScored
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct59
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1159.35
Minimum34
Maximum5947
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size720.0 B
2021-09-08T11:41:26.591204image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum34
5-th percentile37.95
Q1154.5
median444
Q31669.75
95-th percentile4538.85
Maximum5947
Range5913
Interquartile range (IQR)1515.25

Descriptive statistics

Standard deviation1512.063948
Coefficient of variation (CV)1.304234225
Kurtosis2.406122548
Mean1159.35
Median Absolute Deviation (MAD)373.5
Skewness1.75805808
Sum69561
Variance2286337.384
MonotonicityNot monotonic
2021-09-08T11:41:26.718151image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
702
 
3.3%
2021
 
1.7%
1011
 
1.7%
4581
 
1.7%
7161
 
1.7%
371
 
1.7%
711
 
1.7%
971
 
1.7%
2161
 
1.7%
17531
 
1.7%
Other values (49)49
81.7%
ValueCountFrequency (%)
341
1.7%
361
1.7%
371
1.7%
381
1.7%
511
1.7%
621
1.7%
702
3.3%
711
1.7%
971
1.7%
1011
1.7%
ValueCountFrequency (%)
59471
1.7%
59001
1.7%
46311
1.7%
45341
1.7%
43981
1.7%
36801
1.7%
36091
1.7%
32281
1.7%
26831
1.7%
22781
1.7%

BasketGiven
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct60
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1159.233333
Minimum55
Maximum3889
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size720.0 B
2021-09-08T11:41:26.859158image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum55
5-th percentile65.95
Q1236
median632.5
Q32001.25
95-th percentile3377.8
Maximum3889
Range3834
Interquartile range (IQR)1765.25

Descriptive statistics

Standard deviation1163.946914
Coefficient of variation (CV)1.004066119
Kurtosis-0.4510570834
Mean1159.233333
Median Absolute Deviation (MAD)497.5
Skewness0.9581639422
Sum69554
Variance1354772.419
MonotonicityNot monotonic
2021-09-08T11:41:26.989303image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1311
 
1.7%
21801
 
1.7%
31141
 
1.7%
5881
 
1.7%
7201
 
1.7%
17461
 
1.7%
1821
 
1.7%
851
 
1.7%
13711
 
1.7%
6231
 
1.7%
Other values (50)50
83.3%
ValueCountFrequency (%)
551
1.7%
571
1.7%
651
1.7%
661
1.7%
851
1.7%
1151
1.7%
1161
1.7%
1171
1.7%
1311
1.7%
1391
1.7%
ValueCountFrequency (%)
38891
1.7%
37001
1.7%
34691
1.7%
33731
1.7%
33091
1.7%
32301
1.7%
31401
1.7%
31141
1.7%
28471
1.7%
26241
1.7%

TeamLaunch
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct44
Distinct (%)73.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1957.95
Minimum1929
Maximum2016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size720.0 B
2021-09-08T11:41:27.127590image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1929
5-th percentile1929
Q11934.75
median1950.5
Q31977.25
95-th percentile2007.1
Maximum2016
Range87
Interquartile range (IQR)42.5

Descriptive statistics

Standard deviation26.6467316
Coefficient of variation (CV)0.01360950566
Kurtosis-0.7874722188
Mean1957.95
Median Absolute Deviation (MAD)20
Skewness0.6825695415
Sum117477
Variance710.0483051
MonotonicityNot monotonic
2021-09-08T11:41:27.248687image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
192910
 
16.7%
19413
 
5.0%
19392
 
3.3%
19772
 
3.3%
19632
 
3.3%
19352
 
3.3%
19512
 
3.3%
20091
 
1.7%
19621
 
1.7%
19551
 
1.7%
Other values (34)34
56.7%
ValueCountFrequency (%)
192910
16.7%
19301
 
1.7%
19311
 
1.7%
19321
 
1.7%
19331
 
1.7%
19341
 
1.7%
19352
 
3.3%
19392
 
3.3%
19401
 
1.7%
19413
 
5.0%
ValueCountFrequency (%)
20161
1.7%
20141
1.7%
20091
1.7%
20071
1.7%
20041
1.7%
19991
1.7%
19981
1.7%
19961
1.7%
19951
1.7%
19941
1.7%

HighestPositionHeld
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct18
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.05
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size960.0 B
2021-09-08T11:41:27.363775image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q310
95-th percentile17
Maximum20
Range19
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.315232348
Coefficient of variation (CV)0.7539336664
Kurtosis-0.2849335823
Mean7.05
Median Absolute Deviation (MAD)4
Skewness0.8321643556
Sum423
Variance28.25169492
MonotonicityNot monotonic
2021-09-08T11:41:27.467880image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
19
15.0%
46
10.0%
75
8.3%
25
8.3%
65
8.3%
84
 
6.7%
104
 
6.7%
54
 
6.7%
34
 
6.7%
163
 
5.0%
Other values (8)11
18.3%
ValueCountFrequency (%)
19
15.0%
25
8.3%
34
6.7%
46
10.0%
54
6.7%
65
8.3%
75
8.3%
84
6.7%
91
 
1.7%
104
6.7%
ValueCountFrequency (%)
201
 
1.7%
191
 
1.7%
173
5.0%
163
5.0%
151
 
1.7%
141
 
1.7%
122
3.3%
111
 
1.7%
104
6.7%
91
 
1.7%

WinRatio
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct56
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.36466667
Minimum16.67
Maximum59.63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size960.0 B
2021-09-08T11:41:27.603902image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum16.67
5-th percentile21.05
Q127.61
median30.49
Q333.5425
95-th percentile44.706
Maximum59.63
Range42.96
Interquartile range (IQR)5.9325

Descriptive statistics

Standard deviation7.831420519
Coefficient of variation (CV)0.2496892635
Kurtosis3.586127033
Mean31.36466667
Median Absolute Deviation (MAD)3.105
Skewness1.439962145
Sum1881.88
Variance61.33114734
MonotonicityNot monotonic
2021-09-08T11:41:27.726640image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.052
 
3.3%
252
 
3.3%
29.082
 
3.3%
23.332
 
3.3%
57.241
 
1.7%
27.191
 
1.7%
41.111
 
1.7%
24.741
 
1.7%
29.31
 
1.7%
35.071
 
1.7%
Other values (46)46
76.7%
ValueCountFrequency (%)
16.671
1.7%
19.121
1.7%
21.052
3.3%
22.811
1.7%
23.211
1.7%
23.332
3.3%
23.751
1.7%
24.341
1.7%
24.741
1.7%
252
3.3%
ValueCountFrequency (%)
59.631
1.7%
57.241
1.7%
47.481
1.7%
44.561
1.7%
43.771
1.7%
41.181
1.7%
41.111
1.7%
37.531
1.7%
36.81
1.7%
36.11
1.7%

LossRatio
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct58
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.99216667
Minimum20.38
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size960.0 B
2021-09-08T11:41:27.851306image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum20.38
5-th percentile32.0725
Q141.1475
median45.725
Q348.5425
95-th percentile57.5395
Maximum70
Range49.62
Interquartile range (IQR)7.395

Descriptive statistics

Standard deviation8.40111072
Coefficient of variation (CV)0.1867238531
Kurtosis1.959192946
Mean44.99216667
Median Absolute Deviation (MAD)3.89
Skewness-0.2688145903
Sum2699.53
Variance70.57866133
MonotonicityNot monotonic
2021-09-08T11:41:27.986452image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46.252
 
3.3%
502
 
3.3%
51.321
 
1.7%
33.311
 
1.7%
46.971
 
1.7%
47.421
 
1.7%
47.041
 
1.7%
42.871
 
1.7%
53.331
 
1.7%
46.781
 
1.7%
Other values (48)48
80.0%
ValueCountFrequency (%)
20.381
1.7%
22.011
1.7%
29.651
1.7%
32.21
1.7%
32.321
1.7%
33.311
1.7%
36.841
1.7%
37.41
1.7%
37.581
1.7%
38.571
1.7%
ValueCountFrequency (%)
701
1.7%
60.291
1.7%
601
1.7%
57.411
1.7%
56.91
1.7%
55.561
1.7%
53.331
1.7%
52.631
1.7%
51.391
1.7%
51.321
1.7%

WinLossDiffrence
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct60
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-13.6275
Minimum-53.33
Maximum39.25
Zeros0
Zeros (%)0.0%
Negative52
Negative (%)86.7%
Memory size960.0 B
2021-09-08T11:41:28.121138image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-53.33
5-th percentile-31.8345
Q1-21.6875
median-16.23
Q3-7.815
95-th percentile12.5195
Maximum39.25
Range92.58
Interquartile range (IQR)13.8725

Descriptive statistics

Standard deviation15.61885438
Coefficient of variation (CV)-1.146127638
Kurtosis2.987019941
Mean-13.6275
Median Absolute Deviation (MAD)5.995
Skewness1.044275275
Sum-817.65
Variance243.9486123
MonotonicityNot monotonic
2021-09-08T11:41:28.245409image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-17.141
 
1.7%
-17.731
 
1.7%
-17.651
 
1.7%
-22.51
 
1.7%
-14.911
 
1.7%
-17.911
 
1.7%
-11.331
 
1.7%
-53.331
 
1.7%
-7.051
 
1.7%
-21.111
 
1.7%
Other values (50)50
83.3%
ValueCountFrequency (%)
-53.331
1.7%
-41.171
1.7%
-36.671
1.7%
-31.581
1.7%
-29.631
1.7%
-28.951
1.7%
-27.591
1.7%
-26.981
1.7%
-26.671
1.7%
-26.111
1.7%
ValueCountFrequency (%)
39.251
1.7%
35.231
1.7%
17.831
1.7%
12.241
1.7%
10.461
1.7%
8.981
1.7%
4.271
1.7%
0.131
1.7%
-0.781
1.7%
-3.421
1.7%

Interactions

2021-09-08T11:41:03.328039image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:03.457694image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:03.584949image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:03.720569image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:03.877022image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:04.013168image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:04.150113image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:04.288955image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:04.433262image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:04.556682image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:04.703930image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:04.831156image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:04.955857image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:05.071137image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:05.192714image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:05.318546image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:05.442178image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:05.581979image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:05.720300image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:05.850982image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:05.982358image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:06.126970image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:06.253424image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:06.401061image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:06.526972image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:06.650600image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:06.780226image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:06.919922image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:07.046991image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:07.179518image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:07.332078image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:07.483670image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:07.616285image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:07.754920image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:07.904552image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:08.032548image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:08.174202image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:08.306639image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:08.444264image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:08.570734image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:08.703380image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:08.837023image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:08.974449image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:09.111083image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:09.244707image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:09.375735image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:09.547761image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:09.724825image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:09.856024image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:09.995986image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:10.119907image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:10.245968image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:10.382911image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:10.515876image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:10.640948image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:10.768999image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:10.902003image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:11.033941image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:11.159976image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:11.306152image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:11.448954image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:11.576967image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:11.715862image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:12.245983image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:12.375019image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:12.480019image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:12.583955image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:12.687242image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:12.795949image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:12.902931image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:13.008968image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:13.113011image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:13.220011image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:13.337139image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:13.436979image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:13.551004image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:13.650813image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:13.758033image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:13.857831image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:13.960030image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:14.063763image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:14.171001image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:14.281833image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:14.403139image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:14.515980image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:14.627952image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:14.751032image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:14.855964image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:14.973016image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:15.078980image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:15.191007image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:15.296964image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:15.411007image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:15.526010image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:15.643994image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:15.766276image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:15.887984image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:16.005555image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:16.128777image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:16.257938image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:16.371141image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:16.497960image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:16.611881image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:16.728140image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:16.840484image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:16.933552image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:17.026721image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:17.125759image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:17.226629image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:17.328705image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:17.425685image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:17.528452image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:17.640938image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:17.737321image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:17.846057image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:17.940848image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:18.039134image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:18.135050image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:18.245271image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:18.356238image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:18.472479image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:18.591586image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:18.709706image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:18.824440image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:18.943739image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:19.070397image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:19.183095image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:19.307695image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:19.417402image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:19.531087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:19.641946image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:19.736688image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:19.850373image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:19.953107image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:20.089882image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:20.233218image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:20.372012image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:20.492982image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:20.627015image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:20.737959image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:20.857185image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:21.008965image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:21.143987image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:21.258086image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:21.548846image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:21.673124image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:21.802023image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:21.909007image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:22.015044image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:22.118090image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:22.226957image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:22.343963image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:22.445014image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:22.563006image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:22.662028image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:22.766004image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:22.868155image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:22.963067image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:23.059017image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:23.160816image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:23.264060image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:23.368103image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:23.473971image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:23.578999image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:23.691205image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:23.789013image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:23.896933image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:23.991960image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-08T11:41:24.090982image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-09-08T11:41:28.363564image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-09-08T11:41:28.594143image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-09-08T11:41:28.836007image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-09-08T11:41:29.065960image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-09-08T11:41:24.287258image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-09-08T11:41:24.575943image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

TeamTournamentScorePlayedGamesWonGamesDrawnGamesLostGamesBasketScoredBasketGivenTeamLaunchHighestPositionHeldWinRatioLossRatioWinLossDiffrence
0Team 186438527621647552563594731401929159.6320.3839.25
1Team 286426227621581573608590031141929157.2422.0135.23
2Team 380344226141241598775453433091929147.4829.6517.83
3Team 482338626641187616861439834691931144.5632.3212.24
4Team 586336827621209633920463137001929143.7733.3110.46
5Team 67328192408990531887368033731934141.1136.844.27
6Team 782279226269486081070360938891929336.1040.75-4.65
7Team 87025732302864577861322832301929137.5337.400.13
8Team 95821091986698522766268328471939235.1538.57-3.42
9Team 105118841728606440682215924921932135.0739.47-4.40

Last rows

TeamTournamentScorePlayedGamesWonGamesDrawnGamesLostGamesBasketScoredBasketGivenTeamLaunchHighestPositionHeldWinRatioLossRatioWinLossDiffrence
50Team 513719029134812118319531432.2253.33-21.11
51Team 52456722114371531841929629.1751.39-22.22
52Team 53252681718337111619791025.0048.53-23.53
53Team 543425418630971311929833.3355.56-22.23
54Team 55240681314417018219501619.1260.29-41.17
55Team 561353881119365520161721.0550.00-28.95
56Team 571343881020386620092021.0552.63-31.58
57Team 58122307815375719561623.3350.00-26.67
58Team 59119307518518519511623.3360.00-36.67
59Team 60114305421346519551516.6770.00-53.33